E-Commerce Software Resources

Articles, Glossary Terms, Discussions, and Reports to expand your knowledge on E-Commerce Software

Resource pages are designed to give you a cross-section of information we have on specific categories. You'll find articles from our experts, feature definitions, discussions from users like you, and reports from industry data.

Contents

E-Commerce Software Articles

What Is Social Commerce, and Why We Created a New Category for It

The fact that social media is critical for e-commerce is obvious. There are billions of users on social media platforms, and many of them buy online. It is estimated that the number of social media users worldwide will reach 5.85 billion by 2027. In addition, sellers also use social media to share and promote their products—the value of social commerce sales worldwide is estimated at 2.9 trillion dollars by 2026.

by Gabriel Gheorghiu

COVID-19 Drives E-Commerce Growth for Brick and Mortar Retailers

The coronavirus pandemic has compelled consumers to stay home in quarantine, driving some brick and mortar stores to either move online or adopt a combination of both in-store (curbside pickup) and online sales methods for the first time.

by Nathan Calabrese

E-Commerce Software Glossary Terms

Customer Experience Management

Customer experience management (CXM) prioritizes personalized experiences at every customer interaction. Learn more about its benefits for businesses.

by Holly Landis

Headless CMS

A headless content management system is back-end software that allows connection to any presentation layer via application programming interfaces (APIs.)

by Kelly Fiorini

Go-to-Market Strategy

A go-to-market strategy is an all-encompassing plan for launching a new product or service. Learn how to create a GTM strategy and why it’s important.

by Holly Landis

E-Commerce Software Discussions

What are the best e-commerce fraud protection software platforms for transaction filtering?

Hi G2 community, I’m researching the best e-commerce fraud protection software for transaction filtering. The main challenge is finding tools that can stop risky orders without pushing too many legitimate transactions into manual review.

From the G2 Grid report for E-commerce Fraud Protection, I focused on platforms where reviews consistently mention rules, risk scoring, automated decisions, or false-positive control.

What I’m trying to separate is:

tools that give fraud teams more rule control
tools that automate more of the decision
tools that add human review when needed

A few stood out:

Sift: Reviewers use risk scores, velocity, payment data, and linked accounts to filter suspicious transactions.
SEON: Users highlight configurable rules plus device, email, phone, and behavioral signals.
Signifyd: Reviews point to automated order decisions, fewer manual reviews, and lower false-decline risk.
ClearSale: Reviewers mention automated screening backed by human review for borderline cases.

The difference seems to come down to how much control the team wants to keep. SEON gives more rule flexibility, Sift leans heavily on risk signals, Signifyd automates more of the approve-or-decline flow, and ClearSale adds a human layer.

For teams processing a high volume of transactions, which setup works best in practice? Do you prefer tighter control over rules, or fewer manual decisions overall?

E-commerce Fraud Protection reviews on G2 split pretty cleanly by industry, and that split matters more here than the post lets on. SEON reviewers consistently point to the rule engine's flexibility as the reason they can set risk decisions instead of relying on one-size-fits-all thresholds, which aligns with the post's read on it, giving more control. The recurring caveat is that flexibility comes with a real setup and calibration cost, backtesting new rules is described as harder than building them, so the control the post highlights isn't free.

Worth noting that SEON's own review base skews heavily toward gambling and financial services rather than general e-commerce retail, so the transaction patterns behind those glowing reviews may not map cleanly onto a typical online store's fraud profile. For teams processing a high volume of e-commerce transactions specifically, it would help to hear from reviewers closer to that exact profile rather than assuming the gambling industry experience transfers directly.

Answered: Shashank Shastry on September 16, 2026

Your answer

Which e-commerce personalization platforms work without requiring a developer to implement?

I've been researching which e-commerce personalization platforms work without requiring a developer to implement for a small ecommerce team with no engineering resources of their own. The real distinction is whether the platform ships with a visual editor and pre-built integrations for common ecommerce platforms, or whether it expects a developer to write custom tracking code. Here's what stood out in the E-Commerce Personalization category on G2.

Omniconvert. Visual editor for building personalization rules without code, aimed at marketing teams working independently of engineering. Best for marketing teams without developer support.
AB Tasty. No-code visual editor for setting up personalized experiences, positioned for marketers rather than developers. Best for marketers wanting a no-code setup.
GetResponse. Personalization features built into a broader marketing platform already aimed at non-technical users. Best for small teams already using GetResponse for email.
Shopware. Ecommerce platform with personalization built into the storefront itself, reducing the need for a separate implementation project. Best for teams building their storefront on Shopware directly.
Moosend. Marketing automation with personalization features accessible through a visual workflow builder rather than custom code. Best for small teams wanting personalization inside an email-first tool.

Worth checking carefully what "no developer required" actually covers. Basic product recommendation widgets are usually genuinely no-code, but deeper personalization, like personalizing based on custom data fields specific to your business, often needs at least someone comfortable with tag managers even on platforms marketed as fully no-code.

For teams running personalization without developer support, which platform's no-code claims actually held up in practice, and where did you eventually need technical help anyway?

The key test is whether marketers can launch and maintain the common personalization workflows themselves, not whether every use case is code-free. I’d compare visual editing, native ecommerce integrations, preview/testing tools, and how quickly custom data or tracking requirements force the team to involve a developer.

Answered: Darshayita Thakur on September 17, 2026

Your answer

What's the best e-commerce personalization software for product recommendations based on browsing behavior?

I've been mapping out what's the best e-commerce personalization software for product recommendations based on browsing behavior for a store owner whose site showed the same homepage to every visitor regardless of what they'd looked at before. The core distinction is whether recommendations update in real time from actual browsing behavior, or just rely on static rules like "customers also bought." Here's what stood out in the E-Commerce Personalization category on G2.

Dynamic Yield. Real-time behavioral personalization built specifically for product recommendations across the shopping journey. Best for teams wanting deep behavioral targeting.
Bloomreach. Combines product discovery and personalization, using search and browsing signals together to drive recommendations. Best for teams wanting personalization tied to on-site search.
VWO Testing. Personalization paired with A/B testing, useful for teams that want to validate whether personalized recommendations actually lift conversion before rolling them out broadly. Best for teams wanting to test personalization impact directly.
Optimizely Web Experimentation. Experimentation-first platform with personalization features layered on top, aimed at teams already running a testing program. Best for teams with an existing experimentation practice.
AB Tasty. Combines personalization and experimentation with a lower setup barrier than some enterprise platforms. Best for mid-sized teams wanting personalization without enterprise complexity.

Worth being honest that behavioral personalization is only as good as the data feeding it. A store with low traffic often doesn't generate enough browsing signal for the algorithm to personalize meaningfully, so this category tends to pay off more once you have real volume than it does on a brand-new site.

For ecommerce teams running behavioral personalization, did it actually move conversion in a way you could measure, or did it mostly just look impressive without a clear lift?

On the measurable-ROI question: recommendation widgets grade their own homework. They claim credit for every click they touched, including purchases that would have happened anyway, which is why the reported lift and the real lift can be very different numbers. The only clean answer is a holdout, a slice of traffic that never sees recommendations, and it's telling that half the products in this list are experimentation tools; that pairing is the point, not a coincidence.